混在自動運転環境における交通ネットワークの効率的均衡を促す価格設計
Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy
人間の運転者と自動運転ライドヘイリングが混在する並列道路網で、利用者選好を学習しつつ社会的目的を最大化する価格を計画最適化で求める手法を提案した。
著者: Erdem Bıyık, Daniel A. Lazar, Ramtin Pedarsani, Dorsa Sadigh
分類: cs.MA, cs.AI, cs.LG, cs.RO
原文アブストラクト
Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.
